Inductive knowledge graph completion

11 papers with code • 3 benchmarks • 0 datasets

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Most implemented papers

Inductive Relation Prediction by Subgraph Reasoning

kkteru/grail ICML 2020

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.

Inductive Entity Representations from Text via Link Prediction

dfdazac/blp 7 Oct 2020

However, the extent to which these representations learned for link prediction generalize to other tasks is unclear.

RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools

IBM/RuDaS 16 Sep 2019

Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form.

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

alisadeghian/DRUM NeurIPS 2019

Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities.

KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation

THU-KEG/KEPLER 13 Nov 2019

Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text.

Towards Learning Instantiated Logical Rules from Knowledge Graphs

irokin/GPFL 13 Mar 2020

Instantiated rules contain constants extracted from KGs.

Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs

irokin/RuleHierarchy 29 Jun 2020

Many systems have been developed in recent years to mine logical rules from large-scale Knowledge Graphs (KGs), on the grounds that representing regularities as rules enables both the interpretable inference of new facts, and the explanation of known facts.

Relational Message Passing for Fully Inductive Knowledge Graph Completion

zjukg/rmpi 8 Oct 2022

Subgraph reasoning with message passing is a promising and popular solution.

InGram: Inductive Knowledge Graph Embedding via Relation Graphs

bdi-lab/ingram 31 May 2023

In this paper, we propose an INductive knowledge GRAph eMbedding method, InGram, that can generate embeddings of new relations as well as new entities at inference time.

Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis

anilakash/indkgc 14 Aug 2023

The task of inductive knowledge graph completion requires models to learn inference patterns from a training graph, which can then be used to make predictions on a disjoint test graph.